Klasifikasi Objek Dalam Visi Komputer Dengan Analisis Diskriminan

A robotic sensor system is always supported by a computer system called 'computer vision'. The important conceptof computer vision is object classfifi cation. In this study two algorithms for object classifi cation in this system will becompared. Firstly, A simple method that do not need complex computation and that considered as an informal method iscalled binary tree decision structure. This method is based on modest caracteristic decriptors of an object such as verticalline, horizontal line or ellipse line. Unfortunately this method has weakness in recognize an image that contaminated by anoise. Secondly, a more formal method with high variability descriptors. In this contect a multivariate statistical approachnamed discriminant analysis is proposed as an alternative for object classifi cation. This method is operated by computationof a function called Fisher discriminant function that can be used for separating an object. From the data simulation andanalysis for calssifi cation of two object i.e. screw and bolt and three objects i.e. alphabet T,O and S it can be shown thatdiscriminant analysis approach can classify an object better than binary decision algorithm. The superority of discriminantmethod is especially seen when this method is applied for classifi cation of a noisy image of object

The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: selecting emission line galaxies using the Fisher discriminant

We present a new selection technique of producing spectroscopic target catalogues for massive spectroscopic surveys for cosmology. This work was conducted in the context of the extended Baryon Oscillation Spectroscopic Survey (eBOSS), which will use ~200 000 emission line galaxies (ELGs) at 0.6<zspec<1.0 to obtain a precise baryon acoustic oscillation measurement. Our proposed selection technique is based on optical and near-infrared broad-band filter photometry. We used a training sample to define a quantity, the Fisher discriminant (linear combination of colours), which correlates best with the desired properties of the target: redshift and [OII] flux. The proposed selections are simply done by applying a cut on magnitudes and this Fisher discriminant. We used public data and dedicated SDSS spectroscopy to quantify the redshift distribution and [OII] flux of our ELG target selections. We demonstrate that two of our selections fulfil the initial eBOSS/ELG redshift requirements: for a target density of 180 deg^2, ~70% of the selected objects have 0.6<zspec<1.0 and only ~1% of those galaxies in the range 0.6<zspec<1.0 are expected to have a catastrophic zspec estimate. Additionally, the stacked spectra and stacked deep images for those two selections show characteristic features of star-forming galaxies. The proposed approach using the Fisher discriminant could, however, be used to efficiently select other galaxy populations, based on multi-band photometry, providing that spectroscopic information is available. This technique could thus be useful for other future massive spectroscopic surveys such as PFS, DESI, and 4MOST.Comment: Version published in A&

A system identification based approach for pulsed eddy current non-destructive evaluation

This paper is concerned with the development of a new system identification based approach for pulsed eddy current non-destructive evaluation and the use of the new approach in experimental studies to verify its effectiveness and demonstrate its potential in engineering applications

What is the relationship between photospheric flow fields and solar flares?

We estimated photospheric velocities by separately applying the Fourier Local Correlation Tracking (FLCT) and Differential Affine Velocity Estimator (DAVE) methods to 2708 co-registered pairs of SOHO/MDI magnetograms, with nominal 96-minute cadence and ~2" pixels, from 46 active regions (ARs) from 1996-1998 over the time interval t45 when each AR was within 45^o of disk center. For each magnetogram pair, we computed the average estimated radial magnetic field, B; and each tracking method produced an independently estimated flow field, u. We then quantitatively characterized these magnetic and flow fields by computing several extensive and intensive properties of each; extensive properties scale with AR size, while intensive properties do not depend directly on AR size. Intensive flow properties included moments of speeds, horizontal divergences, and radial curls; extensive flow properties included sums of these properties over each AR, and a crude proxy for the ideal Poynting flux, the total |u| B^2. Several magnetic quantities were also computed, including: total unsigned flux; a measure of the amount of unsigned flux near strong-field polarity inversion lines, R; and the total B^2. Next, using correlation and discriminant analysis, we investigated the associations between these properties and flares from the GOES flare catalog, when averaged over both t45 and shorter time windows, of 6 and 24 hours. We found R and total |u| B^2 to be most strongly associated with flares; no intensive flow properties were strongly associated with flares.Comment: 57 pages, 13 figures; revised content; added URL to manuscript with higher-quality image

Multiple solutions to the likelihood equations in the Behrens-Fisher problem

The Behrens-Fisher problem concerns testing the equality of the means of two normal populations with possibly different variances. The null hypothesis in this problem induces a statistical model for which the likelihood function may have more than one local maximum. We show that such multimodality contradicts the null hypothesis in the sense that if this hypothesis is true then the probability of multimodality converges to zero when both sample sizes tend to infinity. Additional results include a finite-sample bound on the probability of multimodality under the null and asymptotics for the probability of multimodality under the alternative

A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination

By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks